Predictors of ISUP upgrading from biopsy to radical prostatectomy: development and internal validation of a preoperative model in a single-center cohort

Predictors of ISUP upgrading from biopsy to radical prostatectomy: development and internal validation of a preoperative model in a single-center cohort | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Predictors of ISUP upgrading from biopsy to radical prostatectomy: development and internal validation of a preoperative model in a single-center cohort Eduardo Rodríguez Araujo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8824534/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background: Biopsy grade may underestimate tumor aggressiveness; a preoperative estimate of upgrading risk could improve counseling and planning. Methods: Retrospective single-center cohort of men with biopsy ISUP 1-3 undergoing radical prostatectomy (2023-2025) with pre-biopsy mpMRI and systematic biopsy. A multivariable logistic regression model using PSA density, number of positive cores, PI-RADS category, clinical T stage (>=cT2b vs =0.90; rule-out sensitivity >=0.90) were evaluated with 2,000-bootstrap internal validation. Results: Among 166 patients, upgrading occurred in 77 (46.4%). The model achieved an apparent AUC of 0.740; optimism-corrected AUC was 0.712 after 2,000 bootstrap resamples. Calibration was excellent (intercept ~0; slope 1.00). The rule-in cutoff was 0.626 (specificity 0.90; sensitivity 0.416; PPV 0.800), and the rule-out cutoff was 0.255 (sensitivity 0.90; specificity 0.281; NPV 0.781). Conclusions: A parsimonious preoperative model integrating PSA density, biopsy tumor burden, PI-RADS, clinical stage, and biopsy ISUP provides moderate discrimination, strong calibration, and interpretable operating points to support risk stratification for upgrading in biopsy ISUP 1-3 patients. ISUP grade group Prostate cancer Prediction model Upgrading Figures Figure 1 Figure 2 Figure 3 Background Prostate cancer is a significant public health issue due to its high prevalence worldwide and its clinical impact in terms of both diagnosis and treatment [ 1 ] In this context, accurate initial stratification is critical for selecting the most appropriate treatment and avoiding both overtreatment and undertreatment. Decision-making is largely based on the histopathological classification derived from the biopsy, particularly on the grading system agreed upon by ISUP, which seeks to standardize tumor grading and improve its reproducibility [ 2 ] However, even with standardized criteria, prostate biopsy represents a partial sample of the tumor and may not accurately reflect intraprostatic heterogeneity. Consequently, it is not uncommon to observe discrepancies between the grade assigned in the biopsy and the definitive grade reported in the radical prostatectomy specimen. This discordance, including Gleason upgrading, has been documented in institutional series and meta-analyses, with direct implications for prognostic estimation and the choice of initial management [ 3 ] Several studies have described the frequency of upgrading and downgrading and identified associated factors, considering elements of the modified grading system and even the impact of tertiary patterns, which underscores that preoperative prediction of upgrading can provide real clinical value [ 4 ] Complementarily, it has been emphasized that pathological reclassification can modify the patient's perception of risk, influence the indication for definitive treatment, and condition the use of complementary therapies (e.g., radiotherapy and/or hormone therapy), in addition to strategies such as active surveillance or the extension of surgery/lymphadenectomy [ 5 ] In recent years, multiparametric magnetic resonance imaging (mpMRI) has taken on a central role in preoperative evaluation, based on standardized reporting systems such as PI-RADS v2, which aim to standardize interpretation, report the risk of clinically significant cancer, and facilitate decision-making [ 6 ] Clinical evidence has shown that mpMRI can improve the detection of clinically significant disease and provide relevant information for diagnosis and treatment planning, compared to strategies based exclusively on systematic biopsy [ 7 ] Altogether, these data support the need for a preoperative approach that integrates routinely available clinical variables, MRI findings, and biopsy grade to estimate the likelihood of upgrading at radical prostatectomy. Therefore, we aimed to develop and internally validate a multivariable prediction model for ISUP upgrading ( ≥ + 1) from biopsy to radical prostatectomy among patients with biopsy ISUP 1–3 in a single-center cohort. Methods Study design and participants. We conducted a retrospective single-center cohort study at the Department of Urologic Oncology, a tertiary referral cancer center, including patients with prostate cancer undergoing radical prostatectomy between 2023 and 2025. For descriptive analyses, all eligible patients were considered (n = 177). For model development, we included patients with biopsy ISUP grade group 1–3 who had pre-biopsy multiparametric MRI, systematic biopsy data, and complete histopathology from both biopsy and prostatectomy (n = 166); patients with biopsy ISUP 4–5 were excluded from model building. Imaging. All patients underwent pre-biopsy multiparametric prostate MRI on a 3-Tesla scanner. The mpMRI protocol included high-resolution T2-weighted imaging, diffusion-weighted imaging with ADC maps, dynamic contrast-enhanced sequences, and T1-weighted imaging, and studies were interpreted using PI-RADS. Pathology. Systematic biopsy specimens were reviewed by pathologists with dedicated training in urologic tumors, and histopathologic grading was assigned according to ISUP criteria. Outcome and predictors. The primary outcome was upgrading, defined as an increase of ≥ 1 ISUP grade group from biopsy to radical prostatectomy (e.g., ISUP 1→2, 2→3, or 3→4). Prespecified preoperative predictors included PSA density (PSAD), number of positive systematic biopsy cores, PI-RADS category on pre-biopsy mpMRI, clinical T stage (≥ cT2b vs ≤cT2a; dichotomized), and biopsy ISUP grade group 1–3. Statistical analysis and software. A multivariable logistic regression model was fitted to estimate the probability of upgrading. Discrimination was assessed using the area under the ROC curve (AUC). Calibration was evaluated using a calibration plot and calibration metrics (intercept/calibration-in-the-large and slope). Internal validation was performed using bootstrap resampling (2,000 resamples) to quantify optimism and obtain optimism-corrected performance estimates. Clinical utility was examined using decision curve analysis across a range of threshold probabilities. Two operating points were additionally defined to facilitate clinical interpretation: a rule-out threshold prioritizing sensitivity ≥ 0.90 and a rule-in threshold prioritizing specificity ≥ 0.90. Data cleaning, descriptive analysis, and association analyses were performed in Python 3.11.2 (pandas 2.2.3, statsmodels 0.14.3, scikit-learn 1.4.2). Predictive modeling and performance evaluation (AUC/ROC, calibration), bootstrap validation, and decision curve analysis were performed in R (R Foundation for Statistical Computing, Vienna, Austria) with statistical packages for modeling and internal validation. Regression coefficients (log-odds) for the final model are provided in Supplementary Table S3 to facilitate calculation of individual predicted risk. AI-assisted language editing: Grammarly and a large language model (ChatGPT, OpenAI) were used to improve grammar and readability. These tools were not used to generate scientific content or to alter the meaning of the results. All changes were reviewed and approved by the authors, who take full responsibility for the manuscript. Secondary (subgroup) analysis was performed among patients with biopsy ISUP 1 (Grade Group 1). Using the same candidate predictors as in the primary model, biopsy ISUP was omitted because it is constant in this subset. A multivariable logistic regression model including PSA density, number of positive cores, PI-RADS category, and clinical stage (≥ cT2b vs ≤cT2a) was fitted. Discrimination (AUC with 95% CI) and calibration were assessed, and internal validation was performed by bootstrap resampling using the same procedure as the primary analysis. Results A total of 177 patients underwent radical prostatectomy during the study period. After applying eligibility criteria, 166 patients (biopsy ISUP grade groups 1–3 with complete predictors) comprised the model development cohort. Baseline demographic and clinical characteristics are summarized in Table 1. Upgrading (≥ +1 ISUP) occurred in 77/166 patients (46.4%) (Table 2). In the overall cohort, the median biopsy-to-prostatectomy interval was 128 days (IQR 90–187; n=172), and 134 days (IQR 93–189; n=161) in the ISUP 1–3 modeling cohort. Maximum core involvement and MRI lesion count are reported to contextualize biopsy sampling and imaging findings (Table 4). Univariable comparisons are presented in Table 3. Upgrading was associated with higher BMI (p=0.049) and higher PSA density (PSAD) (p=0.023) and differed by biopsy ISUP grade group distribution (p=0.010). PI-RADS category showed a trend toward association (p=0.075). Univariable logistic regression estimates are detailed in Table 4; clinical stage (≥T2b vs ≤T2a) also showed a trend (OR 0.57, 95% CI 0.30–1.07; p=0.081). In the multivariable global logistic regression model including PSAD, number of positive cores, PI-RADS category, clinical T stage, and biopsy ISUP (grade groups 1–3), PSAD and PI-RADS remained independent predictors of upgrading. Model discrimination was moderate (apparent AUC 0.740). Internal bootstrap validation (2,000 resamples) estimated optimism of 0.034, yielding an optimism-corrected AUC of 0.712. Calibration was excellent (intercept ~0; slope 1.00), and decision curve analysis demonstrated net benefit across clinically relevant thresholds. Biopsy ISUP 2–3 (vs ISUP 1) was associated with lower odds of upgrading, consistent with a ceiling effect of the ≥+1 upgrading definition. Multivariable regression results are provided in Table 5. Prespecified operating points include the rule-in cutoff of 0.626 and rule-out cutoff of 0.255 with corresponding TP/FP/TN/FN metrics, predictive values, and likelihood ratios. Operating points are summarized in Table 6. In the prespecified biopsy ISUP 1 (Grade Group 1) subgroup (complete-case n=98), the subgroup model (PSA density, positive cores, PI-RADS, and clinical stage ≥cT2b) showed an apparent AUC of 0.750 (95% CI 0.654–0.846). Bootstrap internal validation for this subgroup is provided in the Supplementary material. Performance plots for this subgroup (ROC, calibration, and decision curve analysis) are shown in Supplementary Figures S5–S7, and rule-in/rule-out thresholds are summarized in Supplementary Table S2. Discussion In this single-center cohort (n = 177; model cohort n = 166), upgrading from biopsy to radical prostatectomy was frequent (46.4%), reaffirming that biopsy-based grading can underestimate tumor aggressiveness in a clinically meaningful proportion of patients. Contemporary low-risk surgical series report similar magnitudes of discordance, with upgrading approaching one-half of cases and non-trivial rates of upstaging, emphasizing the practical limitations of relying on biopsy grade alone for definitive counseling and treatment planning. [ 8 ] From a performance perspective, the global model showed adequate discrimination (apparent AUC 0.740) and remained stable after internal bootstrap validation (2,000 resamples), yielding an optimism-corrected AUC of 0.712, with favorable calibration and a signal of clinical utility on decision curve analysis. These estimates are comparable to contemporary tools developed on overlapping domains; for example, a nomogram integrating PSA metrics, biopsy tumor burden, clinical stage, and PI-RADS has reported AUC values in the mid-0.70 range with acceptable internal validation. [ 9 ] While our model relies on routinely available variables, biologic markers may further refine risk in selected patients; notably, PTEN loss on Gleason 6 biopsy has been associated with a higher likelihood of upgrading at prostatectomy, supporting the concept that molecular features may identify a subset at disproportionate risk. [ 10 ] At the diagnostic level, an MRI-based pathway with targeted biopsy increases detection of clinically significant disease and reduces overdiagnosis compared with systematic biopsy alone. [ 11 ] Nevertheless, grade discordance persists even with extended sampling strategies (≥ 12 cores), suggesting that upgrading reflects not only sampling constraints but also intraprostatic heterogeneity and the challenges of capturing the highest-grade component with needle cores. [ 12 ] This framework supports the clinical plausibility of PI-RADS as a contributor to upgrading risk, acting as a lesion-level surrogate for biologic significance that may not be fully captured by PSA-derived metrics or core counts. Regarding predictors, our findings align with a clinically coherent structure: markers of biopsy tumor burden (e.g., number of positive cores) and markers of biologic activity such as PSA density, with additional contributions from clinical stage, PI-RADS, and biopsy ISUP grade group [ 1 – 3 ] The central role of biopsy tumor burden is well supported, as both the number and percentage of positive cores have demonstrated independent and incremental predictive value for adverse pathology and biochemical recurrence after radical prostatectomy. [ 13 ] Complementary morphometric work has shown that quantitative measures of tumor extent in core tissue (fraction of positive cores, overall tumor percentage, and millimetric cancer length) correlate with pathologic stage and oncologic outcomes, reinforcing that “tumor burden” is best captured as a continuous construct rather than a binary variable. [ 14 ] In parallel, PI-RADS has been robustly associated with clinically significant cancer and has remained an independent predictor in contemporary reports, supporting its relevance when risk stratification is performed preoperatively. [ 15 ] More recent evidence further strengthens these same axes. Metabolic syndrome has been associated with higher risks of upgrading and upstaging in radical prostatectomy cohorts, providing a clinically plausible explanation for why patients with adverse metabolic profiles may warrant closer scrutiny even when initial biopsy findings appear favorable. [ 16 ] In the active surveillance setting, a systematic review and meta-analysis identified consistent predictors of reclassification and relapse, supporting the broader rationale for prediction tools aimed at anticipating clinically meaningful upgrading among men initially considered suitable for conservative management. [ 17 ] Likewise, biopsy-estimated tumor volume has been linked to clinically significant upgrading, reinforcing the importance of quantifying cancer extent on biopsy beyond grade alone. [ 18 ] Population-based studies also highlight heterogeneity across patient groups. Differences in upgrading and/or upstaging rates by race/ethnicity have been reported among men labeled low risk as well as among those with intermediate-risk features, underscoring that baseline risk—and therefore model transportability—may vary across settings and populations. [ 19 , 20 ] Additional contemporary benchmarking from national surgical registries has described temporal trends and risk factors for pathologic upgrading among men treated surgically, offering an external reference point for real-world practice patterns. [ 21 ] Finally, even when efforts are made to reduce diagnostic variability through opinion-matched biopsy assessment, clinically relevant predictors of upgrading in grade group 1 remain, supporting the persistent value of structured risk stratification before definitive therapy. [ 22 ] Limitations This study has limitations inherent to its retrospective, single-center design, so there is a risk of selection bias, and generalizations to other populations should be interpreted with caution. Although internal validation was performed using bootstrapping with adequate calibration, the model's performance may be overestimated in the absence of external validation and requires confirmation in independent cohorts. Finally, the categorization of some variables and the limited size of certain categories (e.g., PI-RADS 5) may have increased the uncertainty of some estimates. Conclusion In a contemporary single-center cohort of patients with prostate cancer and ISUP 1–3 biopsy undergoing radical prostatectomy, upgrading was common (46.4%). A parsimonious preoperative model that integrated PSA density, tumor burden on biopsy, PI-RADS, clinical stage, and ISUP grade on biopsy showed moderate discrimination, good calibration after internal validation, and clinical utility through decision curve analysis. Prespecified operating points (rule-out and rule-in) allow the prediction to be translated into interpretable clinical scenarios for counseling and preoperative planning. External validation in independent cohorts is required before widespread implementation. Abbreviations AUC area under the receiver operating characteristic curve DCA decision curve analysis ISUP International Society of Urological Pathology mpMRI multiparametric magnetic resonance imaging PI-RADS Prostate Imaging Reporting and Data System PSA prostate-specific antigen PSAD PSA density RP radical prostatectomy. Declarations Ethics approval and consent to participate This retrospective study used de-identified data collected during routine clinical care. Formal ethics committee review was not sought prior to conducting the analysis. All data were anonymized before analysis and handled in accordance with institutional policies and applicable regulations. The requirement for informed consent was waived due to the retrospective design and -+/9-+use of de-identified data. Declaration of Helsinki compliance This retrospective study was conducted in accordance with the ethical principles of the Declaration of Helsinki. The study used de-identified data obtained from routine clinical care. Informed consent was not required due to the retrospective design and the use of anonymized data, in accordance with institutional policies and applicable regulations. Ethics approval and consent to participate : This retrospective study was reviewed and approved by the Research Ethics Committee (Comité de Ética en Investigación) of Centro Médico Nacional Siglo XXI, Coordinación de Investigación en Salud, Instituto Mexicano del Seguro Social (IMSS), Mexico City, Mexico (CONBIOÉTICA registration No. CONBIOÉTICA-09-CEI-009-20160601 ). Consent to participate Informed consent to participate: Written informed consent to participate was not obtained because this was a retrospective analysis of routinely collected clinical data using de-identified information. No intervention was performed, and no additional contact with patients occurred; therefore, informed consent was not required in accordance with institutional policies and applicable regulations. Consent for publication Not applicable. No identifying images or personal data of participants are presented in this manuscript. Competing interests The authors declare that they have no competing interests. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Acknowledgements Not applicable. Authors Eduardo Rodriguez-Araujo, MD 1* , Seiichi Fuziwara-Ruiz, MD 1 , Juan Carlos Zapoto-Martínez, MD 1 , Eduardo Amaya-Fragoso, MD 1 , Cristopher Hernández-Rodríguez, MD 1, David Alberto Nájera-García, MD 1 , Huber Díaz-Fuentes, MD 1, Luis Aguirre-Amador, MD 1. 0/* ffiliations 1 Department of Urologic Oncology, Hospital de Oncología, Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social (IMSS), Mexico City, Mexico CRediT authorship contribution statement Eduardo Rodriguez-Araujo (ERA): Conceptualization; Methodology; Formal analysis; Visualization; Writing – original draft; Writing – review & editing. Seiichi Fuziwara-Ruiz (SFR): Project administration; Writing – review & editing. Juan Carlos Zapoto-Martínez (JCZM): Supervision; Writing – review & editing. Eduardo Amaya-Fragoso (EAF): Investigation; Data curation. Cristopher Hernández-Rodríguez (CHR): Data curation. David Alberto Nájera-García (DANG): Data curation. Huber Díaz-Fuentes (HDF): Data curation. References Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74:229–263. doi:10.3322/caac.21834 Egevad L, Delahunt B, Srigley JR, Samaratunga H. International Society of Urological Pathology (ISUP) grading of prostate cancer – an ISUP consensus on contemporary grading. APMIS. 2016;124:433–435. doi:10.1111/apm.12533 Cohen MS, Hanley RS, Kurteva T, Ruthazer R, Silverman ML, Sorcini A, et al. Comparing the Gleason prostate biopsy and Gleason prostatectomy grading system: The Lahey Clinic Medical Center experience and an international meta-analysis. Eur Urol. 2008;54:371–381. doi:10.1016/j.eururo.2008.03.049 Epstein JI, Feng Z, Trock BJ, Pierorazio PM. Upgrading and downgrading of prostate cancer from biopsy to radical prostatectomy: incidence and predictive factors using the modified Gleason grading system and factoring in tertiary grades. Eur Urol. 2012;61(5):1019–1024. doi:10.1016/j.eururo.2012.01.050 Freedland SJ, Kane CJ, Amling CL, Aronson WJ, Terris MK, Presti JC Jr. Upgrading and downgrading of prostate needle biopsies: risk factors and clinical implications. Urology. 2007;69(3):495–499. doi:10.1016/j.urology.2006.10.036 Barentsz JO, Weinreb JC, Verma S, Thoeny HC, Tempany CM, Shtern F, et al. Synopsis of the PI-RADS v2 Guidelines for multiparametric prostate magnetic resonance imaging and recommendations for use. Eur Urol. 2016;69(1):41–49. doi:10.1016/j.eururo.2015.08.038 Ahmed HU, El-Shater Bosaily A, Brown LC, Gabe R, Kaplan R, Parmar MK, et al. Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): a paired validating confirmatory study. Lancet. 2017;389:815–822. doi:10.1016/S0140-6736(16)32401-1 Flammia RS, Hoeh B, Hohenhorst L, Sorce G, Chierigo F, Panunzio A, et al. Adverse upgrading and/or upstaging in contemporary low-risk prostate cancer patients. Int Urol Nephrol. 2022;54:2521-2528. doi:10.1007/s11255-022-03250-0. Wang X, Zhang Y, Zhang F, Ji Z, Yang P, Tian Y. Predicting Gleason sum upgrading from biopsy to radical prostatectomy pathology: a new nomogram and its internal validation. BMC Urol. 2021;21:3. doi:10.1186/s12894-020-00773-5. Lotan TL, Carvalho FLF, Peskoe SB, Hicks JL, Good J, Fedor HL, et al. PTEN loss is associated with upgrading of prostate cancer from biopsy to radical prostatectomy. Mod Pathol. 2015;28:128-137. doi:10.1038/modpathol.2014.85. Kasivisvanathan V, Rannikko AS, Borghi M, Panebianco V, Mynderse LA, Vaarala MH, et al. MRI-targeted or standard biopsy for prostate-cancer diagnosis. N Engl J Med. 2018;378:1767-1777. doi:10.1056/NEJMoa1801993. Hong SK, Han BK, Lee ST, Kim SS, Min KE, Jeong SJ, et al. Prediction of Gleason score upgrading in low-risk prostate cancers diagnosed via multi (≥12)-core prostate biopsy. World J Urol. 2009;27:271-276. doi:10.1007/s00345-008-0343-3. Briganti A, Chun FK-H, Hutterer GC, Gallina A, Shariat SF, Salonia A, et al. Systematic assessment of the ability of the number and percentage of positive biopsy cores to predict pathologic stage and biochemical recurrence after radical prostatectomy. Eur Urol. 2007;52:733-745. doi:10.1016/j.eururo.2007.02.054. Brimo F, Vollmer RT, Corcos J, Kotar K, Begin LR, Humphrey PA, et al. Prognostic value of various morphometric measurements of tumour extent in prostate needle core tissue. Histopathology. 2008;53:177-183. doi:10.1111/j.1365-2559.2008.03087.x. Park SY, Jung DC, Oh YT, Cho NH, Choi YD, Rha KH, et al. Prostate cancer: PI-RADS version 2 helps preoperatively predict clinically significant cancers. Radiology. 2016. doi:10.1148/radiol.16151133. De Nunzio C, Brassetti A, Simone G, Lombardo R, Mastroianni R, Collura D, et al. Metabolic syndrome increases the risk of upgrading and upstaging in patients with prostate cancer on biopsy: a radical prostatectomy multicenter cohort study. Prostate Cancer Prostatic Dis. 2018. doi:10.1038/s41391-018-0054-9. Petrelli F, Vavassori I, Cabiddu M, Coinu A, Ghilardi M, Borgonovo K, et al. Predictive factors for reclassification and relapse in prostate cancer eligible for active surveillance: a systematic review and meta-analysis. Urology. 2016. doi:10.1016/j.urology.2016.01.034. Dong F, Jones JS, Stephenson AJ, Magi-Galluzzi C, Reuther AM, Klein EA. Prostate cancer volume at biopsy predicts clinically significant upgrading. J Urol. 2008;179:896-900. doi:10.1016/j.juro.2007.10.060. Maurice MJ, Sundi D, Schaeffer EM, Abouassaly R. Risk of pathological upgrading and upstaging among men with low-risk prostate cancer varies by race: results from the National Cancer Data Base. J Urol. 2016. doi:10.1016/j.juro.2016.08.095. Nocera L, Wenzel M, Collà Ruvolo C, Würnschimmel C, Tian Z, Gandaglia G, et al. The impact of race/ethnicity on upstaging and/or upgrading rates among intermediate risk prostate cancer patients treated with radical prostatectomy. World J Urol. 2021. doi:10.1007/s00345-021-03816-0. Bullock N, Simpkin A, Fowler S, Varma M, Kynaston H, et al. Pathological upgrading in prostate cancer treated with surgery in the United Kingdom: trends and risk factors from the British Association of Urological Surgeons Radical Prostatectomy Registry. BMC Urol. 2019;19:94. doi:10.1186/s12894-019-0526-9. Maruyama Y, Sadahira T, Araki M, Mitsui Y, Wada K, Acosta Gonzalez HR, et al. Factors predicting pathological upgrading after prostatectomy in patients with Gleason grade group 1 prostate cancer based on opinion-matched biopsy specimens. Mol Clin Oncol. 2020;12:384-389. doi:10.3892/mco.2020.1996. Tables Tables 1 to 6 are available in the supplementary files section Additional Declarations No competing interests reported. Supplementary Files SUPPLEMENTARYMATERIAL.docx Tables.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 24 Apr, 2026 Reviewers agreed at journal 24 Apr, 2026 Reviewers invited by journal 07 Apr, 2026 Editor assigned by journal 25 Feb, 2026 Editor invited by journal 18 Feb, 2026 Submission checks completed at journal 17 Feb, 2026 First submitted to journal 17 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8824534","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":623201459,"identity":"8be53671-c858-4cfa-aeff-a24340ea380b","order_by":0,"name":"Eduardo Rodríguez Araujo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIie3PMQrCMBSA4VcC6RLM2lLxDJGC4qJnKV2Du3SoIjgVXAUv4dTJIcHBpe51ayy4Obg72HRzaTo65F9CQj7yAmCz/WEDQGu9jqibtQdkYCIYnJaEflYAiIbgviQ6HXhLwExcuamc89w5+S9Zv/l8iAGpR9lFSLRl8IzRNFjGTOZxMxgOQ945WLTzQCA8O/KJJ3PUEIKDTkKVJilh90KTtAfx2lcuHiuJJpc+RDV/EVfmZzxkt/xKMDL8hdJYViCSdO8W42qVJwvqblXdRXTo87s1XLfZbDabuS9vd0FuaAuWZwAAAABJRU5ErkJggg==","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Eduardo","middleName":"Rodríguez","lastName":"Araujo","suffix":""}],"badges":[],"createdAt":"2026-02-08 23:53:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8824534/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8824534/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106972129,"identity":"693e6d1a-7e9c-4ad6-bfce-7c6bab115d94","added_by":"auto","created_at":"2026-04-15 10:22:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":209540,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curves for predicting ISUP upgrading (binary outcome: ≥+1 ISUP grade group) in the global model cohort (n=166). The red curve corresponds to the complete multivariable logistic regression model (PSA density, number of positive cores, PI-RADS category, clinical T stage, and biopsy ISUP grade group); the remaining curves represent the discrimination of each individual predictor. The AUC of the global model is reported with 95% confidence interval (DeLong). The diagonal line indicates zero discrimination (AUC=0.50).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8824534/v1/a7aed30bff34ff3bafa7e52b.png"},{"id":106972128,"identity":"3b52fe4a-9224-4616-99da-71b34476c231","added_by":"auto","created_at":"2026-04-15 10:22:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":118700,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration plot of the global multivariable model showing apparent and bootstrap bias-corrected calibration against the ideal line (45°). The model was internally validated using bootstrap resampling (2,000 iterations).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8824534/v1/e7bcbc8f90c4ecefeeb555d3.png"},{"id":106972574,"identity":"d0cc0c7a-4cb3-4cfd-a69c-22c3b83d9e52","added_by":"auto","created_at":"2026-04-15 10:23:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":85265,"visible":true,"origin":"","legend":"\u003cp\u003eDecision curve analysis (DCA). Net benefit of the prediction model (solid line) compared with default strategies of evaluating all patients (\"treat all\") or none (\"treat none\") across threshold probabilities. In this study, \"treat\" represents an intensified diagnostic work-up to mitigate pathological ISUP upgrading at radical prostatectomy. Vertical dashed lines indicate the prespecified rule-out (0.255) and rule-in (0.626) thresholds.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8824534/v1/5ebf351496a04751cc091de2.png"},{"id":106973835,"identity":"6ef22928-edae-42ab-bd8c-fa229297fa53","added_by":"auto","created_at":"2026-04-15 10:29:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1006076,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8824534/v1/581ed78a-2b2f-4bc0-811c-207113721394.pdf"},{"id":106972105,"identity":"92673092-181d-4ddb-86e6-e18ef8a36f29","added_by":"auto","created_at":"2026-04-15 10:22:06","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17342,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTARYMATERIAL.docx","url":"https://assets-eu.researchsquare.com/files/rs-8824534/v1/c893b7b16da67202a8d02db1.docx"},{"id":106972573,"identity":"9953b918-caa8-4720-9ccc-3504b8b5955b","added_by":"auto","created_at":"2026-04-15 10:23:41","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":27927,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-8824534/v1/821e2991b8d85907443eef4a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003ePredictors of ISUP upgrading from biopsy to radical prostatectomy: development and internal validation of a preoperative model in a single-center cohort\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eProstate cancer is a significant public health issue due to its high prevalence worldwide and its clinical impact in terms of both diagnosis and treatment [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] In this context, accurate initial stratification is critical for selecting the most appropriate treatment and avoiding both overtreatment and undertreatment.\u003c/p\u003e \u003cp\u003eDecision-making is largely based on the histopathological classification derived from the biopsy, particularly on the grading system agreed upon by ISUP, which seeks to standardize tumor grading and improve its reproducibility [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] However, even with standardized criteria, prostate biopsy represents a partial sample of the tumor and may not accurately reflect intraprostatic heterogeneity.\u003c/p\u003e \u003cp\u003eConsequently, it is not uncommon to observe discrepancies between the grade assigned in the biopsy and the definitive grade reported in the radical prostatectomy specimen. This discordance, including Gleason upgrading, has been documented in institutional series and meta-analyses, with direct implications for prognostic estimation and the choice of initial management [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eSeveral studies have described the frequency of upgrading and downgrading and identified associated factors, considering elements of the modified grading system and even the impact of tertiary patterns, which underscores that preoperative prediction of upgrading can provide real clinical value [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] Complementarily, it has been emphasized that pathological reclassification can modify the patient's perception of risk, influence the indication for definitive treatment, and condition the use of complementary therapies (e.g., radiotherapy and/or hormone therapy), in addition to strategies such as active surveillance or the extension of surgery/lymphadenectomy [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eIn recent years, multiparametric magnetic resonance imaging (mpMRI) has taken on a central role in preoperative evaluation, based on standardized reporting systems such as PI-RADS v2, which aim to standardize interpretation, report the risk of clinically significant cancer, and facilitate decision-making [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] Clinical evidence has shown that mpMRI can improve the detection of clinically significant disease and provide relevant information for diagnosis and treatment planning, compared to strategies based exclusively on systematic biopsy [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eAltogether, these data support the need for a preoperative approach that integrates routinely available clinical variables, MRI findings, and biopsy grade to estimate the likelihood of upgrading at radical prostatectomy. Therefore, we aimed to develop and internally validate a multivariable prediction model for ISUP upgrading (\u0026thinsp;\u0026ge;\u0026thinsp;+\u0026thinsp;1) from biopsy to radical prostatectomy among patients with biopsy ISUP 1\u0026ndash;3 in a single-center cohort.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy design and participants. We conducted a retrospective single-center cohort study at the Department of Urologic Oncology, a tertiary referral cancer center, including patients with prostate cancer undergoing radical prostatectomy between 2023 and 2025. For descriptive analyses, all eligible patients were considered (n\u0026thinsp;=\u0026thinsp;177). For model development, we included patients with biopsy ISUP grade group 1\u0026ndash;3 who had pre-biopsy multiparametric MRI, systematic biopsy data, and complete histopathology from both biopsy and prostatectomy (n\u0026thinsp;=\u0026thinsp;166); patients with biopsy ISUP 4\u0026ndash;5 were excluded from model building.\u003c/p\u003e \u003cp\u003eImaging. All patients underwent pre-biopsy multiparametric prostate MRI on a 3-Tesla scanner. The mpMRI protocol included high-resolution T2-weighted imaging, diffusion-weighted imaging with ADC maps, dynamic contrast-enhanced sequences, and T1-weighted imaging, and studies were interpreted using PI-RADS.\u003c/p\u003e \u003cp\u003ePathology. Systematic biopsy specimens were reviewed by pathologists with dedicated training in urologic tumors, and histopathologic grading was assigned according to ISUP criteria.\u003c/p\u003e \u003cp\u003eOutcome and predictors. The primary outcome was upgrading, defined as an increase of \u0026ge;\u0026thinsp;1 ISUP grade group from biopsy to radical prostatectomy (e.g., ISUP 1\u0026rarr;2, 2\u0026rarr;3, or 3\u0026rarr;4). Prespecified preoperative predictors included PSA density (PSAD), number of positive systematic biopsy cores, PI-RADS category on pre-biopsy mpMRI, clinical T stage (\u0026ge;\u0026thinsp;cT2b vs \u0026le;cT2a; dichotomized), and biopsy ISUP grade group 1\u0026ndash;3.\u003c/p\u003e \u003cp\u003eStatistical analysis and software. A multivariable logistic regression model was fitted to estimate the probability of upgrading. Discrimination was assessed using the area under the ROC curve (AUC). Calibration was evaluated using a calibration plot and calibration metrics (intercept/calibration-in-the-large and slope). Internal validation was performed using bootstrap resampling (2,000 resamples) to quantify optimism and obtain optimism-corrected performance estimates. Clinical utility was examined using decision curve analysis across a range of threshold probabilities. Two operating points were additionally defined to facilitate clinical interpretation: a rule-out threshold prioritizing sensitivity\u0026thinsp;\u0026ge;\u0026thinsp;0.90 and a rule-in threshold prioritizing specificity\u0026thinsp;\u0026ge;\u0026thinsp;0.90. Data cleaning, descriptive analysis, and association analyses were performed in Python 3.11.2 (pandas 2.2.3, statsmodels 0.14.3, scikit-learn 1.4.2). Predictive modeling and performance evaluation (AUC/ROC, calibration), bootstrap validation, and decision curve analysis were performed in R (R Foundation for Statistical Computing, Vienna, Austria) with statistical packages for modeling and internal validation. Regression coefficients (log-odds) for the final model are provided in Supplementary Table S3 to facilitate calculation of individual predicted risk. AI-assisted language editing: Grammarly and a large language model (ChatGPT, OpenAI) were used to improve grammar and readability. These tools were not used to generate scientific content or to alter the meaning of the results. All changes were reviewed and approved by the authors, who take full responsibility for the manuscript.\u003c/p\u003e \u003cp\u003eSecondary (subgroup) analysis was performed among patients with biopsy ISUP 1 (Grade Group 1). Using the same candidate predictors as in the primary model, biopsy ISUP was omitted because it is constant in this subset. A multivariable logistic regression model including PSA density, number of positive cores, PI-RADS category, and clinical stage (\u0026ge;\u0026thinsp;cT2b vs \u0026le;cT2a) was fitted. Discrimination (AUC with 95% CI) and calibration were assessed, and internal validation was performed by bootstrap resampling using the same procedure as the primary analysis.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 177 patients underwent radical prostatectomy during the study period. After applying eligibility criteria, 166 patients (biopsy ISUP grade groups 1–3 with complete predictors) comprised the model development cohort. Baseline demographic and clinical characteristics are summarized in Table 1. Upgrading (≥ +1 ISUP) occurred in 77/166 patients (46.4%) (Table 2). In the overall cohort, the median biopsy-to-prostatectomy interval was 128 days (IQR 90–187; n=172), and 134 days (IQR 93–189; n=161) in the ISUP 1–3 modeling cohort. Maximum core involvement and MRI lesion count are reported to contextualize biopsy sampling and imaging findings (Table 4).\u003c/p\u003e\n\u003cp\u003eUnivariable comparisons are presented in Table 3. Upgrading was associated with higher BMI (p=0.049) and higher PSA density (PSAD) (p=0.023) and differed by biopsy ISUP grade group distribution (p=0.010). PI-RADS category showed a trend toward association (p=0.075). Univariable logistic regression estimates are detailed in Table 4; clinical stage (≥T2b vs ≤T2a) also showed a trend (OR 0.57, 95% CI 0.30–1.07; p=0.081).\u003c/p\u003e\n\u003cp\u003eIn the multivariable global logistic regression model including PSAD, number of positive cores, PI-RADS category, clinical T stage, and biopsy ISUP (grade groups 1–3), PSAD and PI-RADS remained independent predictors of upgrading. Model discrimination was moderate (apparent AUC 0.740). Internal bootstrap validation (2,000 resamples) estimated optimism of 0.034, yielding an optimism-corrected AUC of 0.712. Calibration was excellent (intercept ~0; slope 1.00), and decision curve analysis demonstrated net benefit across clinically relevant thresholds. Biopsy ISUP 2–3 (vs ISUP 1) was associated with lower odds of upgrading, consistent with a ceiling effect of the ≥+1 upgrading definition. Multivariable regression results are provided in Table 5.\u003c/p\u003e\n\u003cp\u003ePrespecified operating points include the rule-in cutoff of 0.626 and rule-out cutoff of 0.255 with corresponding TP/FP/TN/FN metrics, predictive values, and likelihood ratios. Operating points are summarized in Table 6.\u003c/p\u003e\n\u003cp\u003eIn the prespecified biopsy ISUP 1 (Grade Group 1) subgroup (complete-case n=98), the subgroup model (PSA density, positive cores, PI-RADS, and clinical stage ≥cT2b) showed an apparent AUC of 0.750 (95% CI 0.654–0.846). Bootstrap internal validation for this subgroup is provided in the Supplementary material. Performance plots for this subgroup (ROC, calibration, and decision curve analysis) are shown in Supplementary Figures S5–S7, and rule-in/rule-out thresholds are summarized in Supplementary Table S2.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this single-center cohort (n\u0026thinsp;=\u0026thinsp;177; model cohort n\u0026thinsp;=\u0026thinsp;166), upgrading from biopsy to radical prostatectomy was frequent (46.4%), reaffirming that biopsy-based grading can underestimate tumor aggressiveness in a clinically meaningful proportion of patients. Contemporary low-risk surgical series report similar magnitudes of discordance, with upgrading approaching one-half of cases and non-trivial rates of upstaging, emphasizing the practical limitations of relying on biopsy grade alone for definitive counseling and treatment planning. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eFrom a performance perspective, the global model showed adequate discrimination (apparent AUC 0.740) and remained stable after internal bootstrap validation (2,000 resamples), yielding an optimism-corrected AUC of 0.712, with favorable calibration and a signal of clinical utility on decision curve analysis. These estimates are comparable to contemporary tools developed on overlapping domains; for example, a nomogram integrating PSA metrics, biopsy tumor burden, clinical stage, and PI-RADS has reported AUC values in the mid-0.70 range with acceptable internal validation. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] While our model relies on routinely available variables, biologic markers may further refine risk in selected patients; notably, PTEN loss on Gleason 6 biopsy has been associated with a higher likelihood of upgrading at prostatectomy, supporting the concept that molecular features may identify a subset at disproportionate risk. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eAt the diagnostic level, an MRI-based pathway with targeted biopsy increases detection of clinically significant disease and reduces overdiagnosis compared with systematic biopsy alone. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] Nevertheless, grade discordance persists even with extended sampling strategies (\u0026ge;\u0026thinsp;12 cores), suggesting that upgrading reflects not only sampling constraints but also intraprostatic heterogeneity and the challenges of capturing the highest-grade component with needle cores. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] This framework supports the clinical plausibility of PI-RADS as a contributor to upgrading risk, acting as a lesion-level surrogate for biologic significance that may not be fully captured by PSA-derived metrics or core counts.\u003c/p\u003e \u003cp\u003eRegarding predictors, our findings align with a clinically coherent structure: markers of biopsy tumor burden (e.g., number of positive cores) and markers of biologic activity such as PSA density, with additional contributions from clinical stage, PI-RADS, and biopsy ISUP grade group [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] The central role of biopsy tumor burden is well supported, as both the number and percentage of positive cores have demonstrated independent and incremental predictive value for adverse pathology and biochemical recurrence after radical prostatectomy. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] Complementary morphometric work has shown that quantitative measures of tumor extent in core tissue (fraction of positive cores, overall tumor percentage, and millimetric cancer length) correlate with pathologic stage and oncologic outcomes, reinforcing that \u0026ldquo;tumor burden\u0026rdquo; is best captured as a continuous construct rather than a binary variable. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] In parallel, PI-RADS has been robustly associated with clinically significant cancer and has remained an independent predictor in contemporary reports, supporting its relevance when risk stratification is performed preoperatively. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eMore recent evidence further strengthens these same axes. Metabolic syndrome has been associated with higher risks of upgrading and upstaging in radical prostatectomy cohorts, providing a clinically plausible explanation for why patients with adverse metabolic profiles may warrant closer scrutiny even when initial biopsy findings appear favorable. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] In the active surveillance setting, a systematic review and meta-analysis identified consistent predictors of reclassification and relapse, supporting the broader rationale for prediction tools aimed at anticipating clinically meaningful upgrading among men initially considered suitable for conservative management. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] Likewise, biopsy-estimated tumor volume has been linked to clinically significant upgrading, reinforcing the importance of quantifying cancer extent on biopsy beyond grade alone. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/p\u003e \u003cp\u003ePopulation-based studies also highlight heterogeneity across patient groups. Differences in upgrading and/or upstaging rates by race/ethnicity have been reported among men labeled low risk as well as among those with intermediate-risk features, underscoring that baseline risk\u0026mdash;and therefore model transportability\u0026mdash;may vary across settings and populations. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] Additional contemporary benchmarking from national surgical registries has described temporal trends and risk factors for pathologic upgrading among men treated surgically, offering an external reference point for real-world practice patterns. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] Finally, even when efforts are made to reduce diagnostic variability through opinion-matched biopsy assessment, clinically relevant predictors of upgrading in grade group 1 remain, supporting the persistent value of structured risk stratification before definitive therapy. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/p\u003e\n\u003ch3\u003eLimitations\u003c/h3\u003e\n\u003cp\u003eThis study has limitations inherent to its retrospective, single-center design, so there is a risk of selection bias, and generalizations to other populations should be interpreted with caution. Although internal validation was performed using bootstrapping with adequate calibration, the model's performance may be overestimated in the absence of external validation and requires confirmation in independent cohorts. Finally, the categorization of some variables and the limited size of certain categories (e.g., PI-RADS 5) may have increased the uncertainty of some estimates.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn a contemporary single-center cohort of patients with prostate cancer and ISUP 1\u0026ndash;3 biopsy undergoing radical prostatectomy, upgrading was common (46.4%). A parsimonious preoperative model that integrated PSA density, tumor burden on biopsy, PI-RADS, clinical stage, and ISUP grade on biopsy showed moderate discrimination, good calibration after internal validation, and clinical utility through decision curve analysis. Prespecified operating points (rule-out and rule-in) allow the prediction to be translated into interpretable clinical scenarios for counseling and preoperative planning. External validation in independent cohorts is required before widespread implementation.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003earea under the receiver operating characteristic curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edecision curve analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eISUP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInternational Society of Urological Pathology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003empMRI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emultiparametric magnetic resonance imaging\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePI-RADS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProstate Imaging Reporting and Data System\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePSA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprostate-specific antigen\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePSAD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePSA density\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eradical prostatectomy.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective study used de-identified data collected during routine clinical care. Formal ethics committee review was not sought prior to conducting the analysis. All data were anonymized before analysis and handled in accordance with institutional policies and applicable regulations. The requirement for informed consent was waived due to the retrospective design and -+/9-+use of de-identified data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Helsinki compliance\u003c/strong\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003cbr\u003e\u003c/strong\u003eThis retrospective study was conducted in accordance with the ethical principles of the Declaration of Helsinki. The study used de-identified data obtained from routine clinical care. Informed consent was not required due to the retrospective design and the use of anonymized data, in accordance with institutional policies and applicable regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e This retrospective study was reviewed and approved by the\u0026nbsp;\u003cstrong\u003eResearch Ethics Committee (Comit\u0026eacute; de \u0026Eacute;tica en Investigaci\u0026oacute;n) of Centro M\u0026eacute;dico Nacional Siglo XXI, Coordinaci\u0026oacute;n de Investigaci\u0026oacute;n en Salud, Instituto Mexicano del Seguro Social (IMSS), Mexico City, Mexico\u003c/strong\u003e (CONBIO\u0026Eacute;TICA registration No.\u0026nbsp;\u003cstrong\u003eCONBIO\u0026Eacute;TICA-09-CEI-009-20160601\u003c/strong\u003e\u003cstrong\u003e).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent to participate:\u003c/strong\u003e Written informed consent to participate was not obtained because this was a retrospective analysis of routinely collected clinical data using de-identified information. No intervention was performed, and no additional contact with patients occurred; therefore, informed consent was not required in accordance with institutional policies and applicable regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. No identifying images or personal data of participants are presented in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEduardo Rodriguez-Araujo, MD\u003csup\u003e1*\u003c/sup\u003e, Seiichi Fuziwara-Ruiz, MD\u003csup\u003e1\u003c/sup\u003e, Juan Carlos Zapoto-Mart\u0026iacute;nez, MD\u003csup\u003e1\u003c/sup\u003e, Eduardo Amaya-Fragoso, MD\u003csup\u003e1\u003c/sup\u003e, Cristopher Hern\u0026aacute;ndez-Rodr\u0026iacute;guez, MD\u003csup\u003e1,\u0026nbsp;\u003c/sup\u003eDavid Alberto N\u0026aacute;jera-Garc\u0026iacute;a, MD\u003csup\u003e1\u003c/sup\u003e, Huber D\u0026iacute;az-Fuentes, MD\u003csup\u003e1,\u003c/sup\u003e Luis Aguirre-Amador, MD\u003csup\u003e1.\u003c/sup\u003e0/*\u003cstrong\u003effiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1\u0026nbsp;\u003c/strong\u003eDepartment of Urologic Oncology, Hospital de Oncolog\u0026iacute;a, Centro M\u0026eacute;dico Nacional Siglo XXI, Instituto Mexicano del Seguro Social (IMSS), Mexico City, Mexico\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eEduardo Rodriguez-Araujo (ERA):\u003c/strong\u003e Conceptualization; Methodology; Formal analysis; Visualization; Writing \u0026ndash; original draft; Writing \u0026ndash; review \u0026amp; editing.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSeiichi Fuziwara-Ruiz (SFR):\u003c/strong\u003e Project administration; Writing \u0026ndash; review \u0026amp; editing.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eJuan Carlos Zapoto-Mart\u0026iacute;nez (JCZM):\u003c/strong\u003e Supervision; Writing \u0026ndash; review \u0026amp; editing.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eEduardo Amaya-Fragoso (EAF):\u003c/strong\u003e Investigation; Data curation.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eCristopher Hern\u0026aacute;ndez-Rodr\u0026iacute;guez (CHR):\u003c/strong\u003e Data curation.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eDavid Alberto N\u0026aacute;jera-Garc\u0026iacute;a (DANG):\u003c/strong\u003e Data curation.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eHuber D\u0026iacute;az-Fuentes (HDF):\u003c/strong\u003e Data curation.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74:229\u0026ndash;263. doi:10.3322/caac.21834\u003c/li\u003e\n \u003cli\u003eEgevad L, Delahunt B, Srigley JR, Samaratunga H. International Society of Urological Pathology (ISUP) grading of prostate cancer \u0026ndash; an ISUP consensus on contemporary grading. APMIS. 2016;124:433\u0026ndash;435. doi:10.1111/apm.12533\u003c/li\u003e\n \u003cli\u003eCohen MS, Hanley RS, Kurteva T, Ruthazer R, Silverman ML, Sorcini A, et al. Comparing the Gleason prostate biopsy and Gleason prostatectomy grading system: The Lahey Clinic Medical Center experience and an international meta-analysis. Eur Urol. 2008;54:371\u0026ndash;381. doi:10.1016/j.eururo.2008.03.049\u003c/li\u003e\n \u003cli\u003eEpstein JI, Feng Z, Trock BJ, Pierorazio PM. Upgrading and downgrading of prostate cancer from biopsy to radical prostatectomy: incidence and predictive factors using the modified Gleason grading system and factoring in tertiary grades. Eur Urol. 2012;61(5):1019\u0026ndash;1024. doi:10.1016/j.eururo.2012.01.050\u003c/li\u003e\n \u003cli\u003eFreedland SJ, Kane CJ, Amling CL, Aronson WJ, Terris MK, Presti JC Jr. Upgrading and downgrading of prostate needle biopsies: risk factors and clinical implications. Urology. 2007;69(3):495\u0026ndash;499. doi:10.1016/j.urology.2006.10.036\u003c/li\u003e\n \u003cli\u003eBarentsz JO, Weinreb JC, Verma S, Thoeny HC, Tempany CM, Shtern F, et al. Synopsis of the PI-RADS v2 Guidelines for multiparametric prostate magnetic resonance imaging and recommendations for use. Eur Urol. 2016;69(1):41\u0026ndash;49. doi:10.1016/j.eururo.2015.08.038\u003c/li\u003e\n \u003cli\u003eAhmed HU, El-Shater Bosaily A, Brown LC, Gabe R, Kaplan R, Parmar MK, et al. Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): a paired validating confirmatory study. Lancet. 2017;389:815\u0026ndash;822. doi:10.1016/S0140-6736(16)32401-1\u003c/li\u003e\n \u003cli\u003eFlammia RS, Hoeh B, Hohenhorst L, Sorce G, Chierigo F, Panunzio A, et al. Adverse upgrading and/or upstaging in contemporary low-risk prostate cancer patients. Int Urol Nephrol. 2022;54:2521-2528. doi:10.1007/s11255-022-03250-0.\u003c/li\u003e\n \u003cli\u003eWang X, Zhang Y, Zhang F, Ji Z, Yang P, Tian Y. Predicting Gleason sum upgrading from biopsy to radical prostatectomy pathology: a new nomogram and its internal validation. BMC Urol. 2021;21:3. doi:10.1186/s12894-020-00773-5.\u003c/li\u003e\n \u003cli\u003eLotan TL, Carvalho FLF, Peskoe SB, Hicks JL, Good J, Fedor HL, et al. PTEN loss is associated with upgrading of prostate cancer from biopsy to radical prostatectomy. Mod Pathol. 2015;28:128-137. doi:10.1038/modpathol.2014.85.\u003c/li\u003e\n \u003cli\u003eKasivisvanathan V, Rannikko AS, Borghi M, Panebianco V, Mynderse LA, Vaarala MH, et al. MRI-targeted or standard biopsy for prostate-cancer diagnosis. N Engl J Med. 2018;378:1767-1777. doi:10.1056/NEJMoa1801993.\u003c/li\u003e\n \u003cli\u003eHong SK, Han BK, Lee ST, Kim SS, Min KE, Jeong SJ, et al. Prediction of Gleason score upgrading in low-risk prostate cancers diagnosed via multi (\u0026ge;12)-core prostate biopsy. World J Urol. 2009;27:271-276. doi:10.1007/s00345-008-0343-3.\u003c/li\u003e\n \u003cli\u003eBriganti A, Chun FK-H, Hutterer GC, Gallina A, Shariat SF, Salonia A, et al. Systematic assessment of the ability of the number and percentage of positive biopsy cores to predict pathologic stage and biochemical recurrence after radical prostatectomy. Eur Urol. 2007;52:733-745. doi:10.1016/j.eururo.2007.02.054.\u003c/li\u003e\n \u003cli\u003eBrimo F, Vollmer RT, Corcos J, Kotar K, Begin LR, Humphrey PA, et al. Prognostic value of various morphometric measurements of tumour extent in prostate needle core tissue. Histopathology. 2008;53:177-183. doi:10.1111/j.1365-2559.2008.03087.x.\u003c/li\u003e\n \u003cli\u003ePark SY, Jung DC, Oh YT, Cho NH, Choi YD, Rha KH, et al. Prostate cancer: PI-RADS version 2 helps preoperatively predict clinically significant cancers. Radiology. 2016. doi:10.1148/radiol.16151133.\u003c/li\u003e\n \u003cli\u003eDe Nunzio C, Brassetti A, Simone G, Lombardo R, Mastroianni R, Collura D, et al. Metabolic syndrome increases the risk of upgrading and upstaging in patients with prostate cancer on biopsy: a radical prostatectomy multicenter cohort study. Prostate Cancer Prostatic Dis. 2018. doi:10.1038/s41391-018-0054-9.\u003c/li\u003e\n \u003cli\u003ePetrelli F, Vavassori I, Cabiddu M, Coinu A, Ghilardi M, Borgonovo K, et al. Predictive factors for reclassification and relapse in prostate cancer eligible for active surveillance: a systematic review and meta-analysis. Urology. 2016. doi:10.1016/j.urology.2016.01.034.\u003c/li\u003e\n \u003cli\u003eDong F, Jones JS, Stephenson AJ, Magi-Galluzzi C, Reuther AM, Klein EA. Prostate cancer volume at biopsy predicts clinically significant upgrading. J Urol. 2008;179:896-900. doi:10.1016/j.juro.2007.10.060.\u003c/li\u003e\n \u003cli\u003eMaurice MJ, Sundi D, Schaeffer EM, Abouassaly R. Risk of pathological upgrading and upstaging among men with low-risk prostate cancer varies by race: results from the National Cancer Data Base. J Urol. 2016. doi:10.1016/j.juro.2016.08.095.\u003c/li\u003e\n \u003cli\u003eNocera L, Wenzel M, Coll\u0026agrave; Ruvolo C, W\u0026uuml;rnschimmel C, Tian Z, Gandaglia G, et al. The impact of race/ethnicity on upstaging and/or upgrading rates among intermediate risk prostate cancer patients treated with radical prostatectomy. World J Urol. 2021. doi:10.1007/s00345-021-03816-0.\u003c/li\u003e\n \u003cli\u003eBullock N, Simpkin A, Fowler S, Varma M, Kynaston H, et al. Pathological upgrading in prostate cancer treated with surgery in the United Kingdom: trends and risk factors from the British Association of Urological Surgeons Radical Prostatectomy Registry. BMC Urol. 2019;19:94. doi:10.1186/s12894-019-0526-9.\u003c/li\u003e\n \u003cli\u003eMaruyama Y, Sadahira T, Araki M, Mitsui Y, Wada K, Acosta Gonzalez HR, et al. Factors predicting pathological upgrading after prostatectomy in patients with Gleason grade group 1 prostate cancer based on opinion-matched biopsy specimens. Mol Clin Oncol. 2020;12:384-389. doi:10.3892/mco.2020.1996.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 6 are available in the supplementary files section\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-urology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"buro","sideBox":"Learn more about [BMC Urology](http://bmcurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/buro/default.aspx","title":"BMC Urology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"ISUP grade group, Prostate cancer, Prediction model, Upgrading","lastPublishedDoi":"10.21203/rs.3.rs-8824534/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8824534/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: Biopsy grade may underestimate tumor aggressiveness; a preoperative estimate of upgrading risk could improve counseling and planning.\u003c/p\u003e\n\u003cp\u003eMethods: Retrospective single-center cohort of men with biopsy ISUP 1-3 undergoing radical prostatectomy (2023-2025) with pre-biopsy mpMRI and systematic biopsy. A multivariable logistic regression model using PSA density, number of positive cores, PI-RADS category, clinical T stage (\u0026gt;=cT2b vs \u0026lt;=cT2a), and biopsy ISUP was developed. Discrimination (AUC), calibration (intercept/slope), decision curve analysis, and two prespecified operating points (rule-in specificity \u0026gt;=0.90; rule-out sensitivity \u0026gt;=0.90) were evaluated with 2,000-bootstrap internal validation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Among 166 patients, upgrading occurred in 77 (46.4%). The model achieved an apparent AUC of 0.740; optimism-corrected AUC was 0.712 after 2,000 bootstrap resamples. Calibration was excellent (intercept ~0; slope 1.00). The rule-in cutoff was 0.626 (specificity 0.90; sensitivity 0.416; PPV 0.800), and the rule-out cutoff was 0.255 (sensitivity 0.90; specificity 0.281; NPV 0.781).\u003c/p\u003e\n\u003cp\u003eConclusions: A parsimonious preoperative model integrating PSA density, biopsy tumor burden, PI-RADS, clinical stage, and biopsy ISUP provides moderate discrimination, strong calibration, and interpretable operating points to support risk stratification for upgrading in biopsy ISUP 1-3 patients.\u003c/p\u003e","manuscriptTitle":"Predictors of ISUP upgrading from biopsy to radical prostatectomy: development and internal validation of a preoperative model in a single-center cohort","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-15 10:12:45","doi":"10.21203/rs.3.rs-8824534/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-24T12:23:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"134001052253284816394161473463814640058","date":"2026-04-24T12:14:49+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-07T20:54:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-25T20:23:07+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-18T18:51:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-18T00:35:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Urology","date":"2026-02-18T00:31:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-urology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"buro","sideBox":"Learn more about [BMC Urology](http://bmcurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/buro/default.aspx","title":"BMC Urology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f40e8bd9-89d1-4687-9b60-a6b655d62e3a","owner":[],"postedDate":"April 15th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-15T10:12:45+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-15 10:12:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8824534","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8824534","identity":"rs-8824534","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}